LOM unifies ontology construction, semantic alignment, and deterministic reasoning in one architecture, reporting 88.8% accuracy on ontology completion and 94% on complex graph reasoning tasks.
Flexner: A flexible lstm-cnn stack framework for named entity recognition, in: CCF International ConferenceonNaturalLanguageProcessingandChineseComputing, Springer
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LOM-action uses business events to drive ontology-governed graph simulations that generate auditable decisions, reporting 93.82% accuracy and 98.74% tool-chain F1 versus 24-36% F1 for frontier LLMs.
citing papers explorer
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Unifying Ontology Construction and Semantic Alignment for Deterministic Enterprise Reasoning at Scale
LOM unifies ontology construction, semantic alignment, and deterministic reasoning in one architecture, reporting 88.8% accuracy on ontology completion and 94% on complex graph reasoning tasks.
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From Business Events to Auditable Decisions: Ontology-Governed Graph Simulation for Enterprise AI
LOM-action uses business events to drive ontology-governed graph simulations that generate auditable decisions, reporting 93.82% accuracy and 98.74% tool-chain F1 versus 24-36% F1 for frontier LLMs.